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master's thesis
Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom
Zagreb: University of Zagreb, Faculty of Science, 2015. urn:nbn:hr:217:826502
Dodik, Anto
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

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Dodik, A. (2015). Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:826502

Dodik, Anto. "Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom." Master's thesis, University of Zagreb, Faculty of Science, 2015. https://urn.nsk.hr/urn:nbn:hr:217:826502

Dodik, Anto. "Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom." Master's thesis, University of Zagreb, Faculty of Science, 2015. https://urn.nsk.hr/urn:nbn:hr:217:826502

Dodik, A. (2015). 'Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:826502

Dodik A. Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2015 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:826502

A. Dodik, "Modeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:217:826502

Metadata
TitleModeliranje dijabetesa pomoću logističke regresije s nominalnom zavisnom varijablom
AuthorAnto Dodik
MentorAnamarija Jazbec (mentor)
Committee memberAnamarija Jazbec (predsjednik povjerenstva)
Committee memberMiljenko Marušić (član povjerenstva)
Committee memberVjekoslav Kovač (član povjerenstva)
Committee memberZvonimir Bujanović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2015-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Logistička regresija, kao moćan statistički alat, koristi se u istraživačkim problemima koji uključuju kategorijsku zavisnu varijablu. Ista je jako robusna regresijska metoda, jer zahtjeva minimalan skup temeljnih pretpostavki. Rezultat je širok raspon primjene, kako u znanstvenim krugovima tako i u kontekstu prakse. U ovom radu objašnjeni su glavni koncepti logističke regresijske analize. Između ostalog, obrađeni su pojmovi omjera šanse, metode maksimalne vjerodostojnosti te logističkog regresijskog modela. Logistički regresijski model koristi se za predviđanje vjerojatnosti događaja putem prilagođavanja podataka logističkoj krivulji. Teorijska podloga logističke regresije sa dihotomnom zavisnom varijablom implementirana je u modeliranju dijabetesa tip 1, odnosno tip 2. Analizirana je baza podataka sa predavanja iz kolegija Odabrane statističke metode u biomedicini. Na temelju univarijatne, odnosno multivarijatne logističke regresije, kao i Stepwise procedure u SAS-u, pronađeni su najadekvatniji modeli koji predviđaju pojavnost dijabetesa tip 1, odnosno tip 2. Ispostavilo se da su hemoglobin i dijastolički krvni tlak glavni prediktori za pojavnost dijabetesa tip 1, dok su hemoglobin i glukoza prediktori za pojavnost dijabetesa tip 2.
Abstract (english)
Logistic regression, a powerful statistic tool, is used in researches which include categorical dependent variable. Logistic regression is robust thanks to the fact that it requires a minimum set of assumptions. As a result, it can be used widely, in scientific as well as in practical context. This thesis describes the basic concepts of logistic regression. Among others, the terms of odds ratio, maximum likelihood estimation and logistic regression model have been analysed. Logistic regression model is used to predict the probability of the event occurring by adjusting the data to the logistic curve. Theoretical background of logistic regression with dichotomous dependent variable is implemented in modeling of type 1 and type 2 diabetes. The database from the course Statistical methods in Biomedical Research has been examined. Based on univariate and multivariate logistic regression, as well as on Stepwise procedure in SAS, the most adequate model for the prediction of type 1 and type 2 diabetes has been found. The results have shown that hemoglobin and diastolic blood pressure are the main predictors of type 1 diabetes, while the hemoglobin and glucose are predictors for type 2 diabetes.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:826502
Study programmeTitle: Mathematical Statistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2019-01-08 11:50:27